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Gemma 1.1 7B It AWQ by TechxGenus

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Gemma 1.1 7B It AWQ is an open-source language model by TechxGenus. Features: 7b LLM, VRAM: 7.2GB, Context: 8K, License: gemma, Quantized, HF Score: 59.8, LLM Explorer Score: 0.15, Arc: 60, HellaSwag: 76.2, MMLU: 60.4, TruthfulQA: 50.4, WinoGrande: 69.9, GSM8K: 41.8.

  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   4-bit   Autotrain compatible   Awq   Conversational   Endpoints compatible   Gemma   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TechxGenus/gemma-1.1-7b-it-AWQ 

Gemma 1.1 7B It AWQ Benchmarks

ARC: 59.98 vs 96.7 (so35)-38%
HellaSwag: 76.21 vs 95.3 (gpt4)-20%
MMLU: 60.39 vs 88.3 (so35)-31.6%
TruthfulQA: 50.4 vs 59 (gpt4)-14.6%
WinoGrande: 69.93 vs 87.5 (gpt4)-20.1%
GSM8K: 41.77 vs 96.4 (so35)-56.7%
LLME Score: 0.14818
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 1.1 7B It AWQ (TechxGenus/gemma-1.1-7b-it-AWQ)
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Gemma 1.1 7B It AWQ Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Content Creation and Communication, Research and Education
Applications:
Chatbots and Conversational AI, Text Summarization
Primary Use Cases:
Text Generation, Natural Language Processing (NLP) Research
Limitations:
Training Data Bias, Complex Task Handling, Factual Accuracy
Supported Languages 
English (High proficiency)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
Novel RLHF method
Hardware Used:
TPUv5e
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming testing
Risk Categories:
Text-to-Text Content Safety, Representational Harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
Input data pre-processing and evaluations reported.
Transparency:
Model details are summarized in this card.
Mitigation Strategies:
Generated guidelines and tools for responsible use.
Input Output 
Input Format:
Text string, such as a question, a prompt, or a document to be summarized.
Accepted Modalities:
text
Output Format:
Generated English-language text in response.
Performance Tips:
Developers should consider configurations such as precision settings for optimal performance.
Release Notes 
Version:
1.1 7B IT
Notes:
This is Gemma 1.1 7B (IT), an update over the original instruction-tuned Gemma release. Introduced novel RLHF method and fixed some response bugs.
LLM NameGemma 1.1 7B It AWQ
Repository 🤗https://huggingface.co/TechxGenus/gemma-1.1-7b-it-AWQ 
Base Model(s)  unsloth/gemma-1.1-7b-it   unsloth/gemma-1.1-7b-it
Model Size7b
Required VRAM7.2 GB
Updated2025-09-23
MaintainerTechxGenus
Model Typegemma
Model Files  6.6 GB: 1-of-2   0.6 GB: 2-of-2
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureGemmaForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.39.3
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TechxGenus/gemma-1.1-7b-it-AWQ.

Rank the Gemma 1.1 7B It AWQ Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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